Another line of augmentation methods focuses on generating a series of prediction pretext tasks that form pseudo-labels by specific transformation, such as prediction of orderings [1]}, [2]} and transformations estimation [3]}, [4]}, [5]}, [6]}. In contrast with data augmentation, all these transformations break the invariant property, i.e., domain augmentation is exactly an adequate transformation in pretext task. Thus, both vertically flipping and rotation could be used in transformations prediction tasks for ImageNet [4]}, because vertically flipping and rotation are domain augmentation for ImageNet by changing the canonical `up-standing' orientation. Nevertheless, the rotation transformations are data augmentation for some orientation agnostic datasets, e.g., medical cell images or satellite images. Regarding this cases, the pseudo-labels provided by such transformation can be seen as noisy labels. The pretext task of classifying such noisy labels is ill-posed and would degrade performance.
